Structured Data vs Unstructured Data: What’s the Difference and Why Does It Matter?

June 8, 2022 | min

Today’s businesses run on data. In fact, the most successful enterprises have found efficient ways to use data to gain deep insight into their operations and processes.

The challenge is storing, managing and securing data that is growing exponentially and putting strains on the traditional storage systems.

The first step to managing data is to understand the different types of data and why those differences matter. The two main types of data are structured and unstructured. Both types can help your business in many ways, even though there are vast differences in the way they’re organized and managed.

What is structured data?

Structured data is information that can be neatly organized into a set structure, such as a spreadsheet with rows and columns. Think of the information we are most used to working with on a computer: customer or patient names and addresses, phone numbers, credit card numbers and expiration dates, Social Security numbers, financial transactions and product names and SKU numbers. These are all good examples of structured data.

Structured data is easily searchable and highly organized, and machines can process it easily. Users can enter data, search through databases, and modify and use it how they want, typically using a relational database management system or through structured query language (SQL), which is a programming language designed specifically for managing structured data.

Benefits of structured data

Structured data is easy to categorize and organize. It is simple to store and access and simpler to analyze – which helps lead to valuable insights. When structured data is used online, it makes important information and websites easier to find. It can also:

  • Aid in the analysis of content

  • Accelerate online searches

  • Enhance search engine optimization (SEO) through the use of text string and attribute indexing

  • Provide users with novel ways to display content

  • Simplify data mining and the extraction of valuable insights from data

  • Make data updates and deletions quite simple because of how well organized it is

  • Simplify business Intelligence operations such as data warehousing.

  • Scale easily and be simple to secure

Characteristics of structured data

Structured data:

  • Conforms to a specific data model and its structure is easily identified

  • Is typically stored in rows and columns 

  • Has known definitions, formats, and meaning

  • Resides in fields that are typically static and fixed within files or records

  • Is easy to group together based on similar classes or relationships – and all data in a group will have identical attributes

  • Makes access and querying easy, which means other applications can easily use the data

  • Has elements that are addressable, making analysis and processing very efficient

  • Is always very specific and stored in a predefined format

  • Is more commonly stored in data warehouses rather than data lakes

Examples of structured data

The most common examples of structured data would be a relational database, such as those used in placing retail product orders, making hotel reservations, or setting up a checking or savings account. The relational databases and the structured data are typically consumed by applications such as ERP, CRM, MDM, EMI, etc.

Any use case that involves spreadsheets is a use case for structured data. For example:

  • Inventory control systems

  • Point-of-sale and retail transaction data

  • Online reservation systems for hotels, airlines, concerts, or other events. 

  • General accounting practices

  • Online banking

  • Customer Relationship Management (CRM)

  • Enterprise Resource Planning (ERP)

  • Enterprise Patient Master Index (EPMI, EMI)

Pros and cons of structured data

The primary advantages of structured data are:

  • It’s human-readable - humans can easily read and visualize the structured data that is neatly organized into rows and columns.

  • It’s easy for machines to process, manipulate, and query. Structured data is easily understood by machines, and its organization and specifically makes it ideal for machine learning datasets.

  • It doesn’t require specialized training to use. Enterprise employees, for instance, are familiar with this type of data and database structure, and don’t need to understand the foundational relationships to use it and benefit from it.

  • It’s been around the longest with many available tools —long before computers made it standard—so developers have created many tools and platforms for storing, using, managing, and analyzing structured data.

  • It’s easy to secure - database vendors have developed inbuilt controls to secure the structured data against cyber threats both intentional and unintentional.

There are also some drawbacks to using structured data, which include:

  • An overall lack of data flexibility. Due to its specific, predefined nature, structured data can typically only be used for its original intended purpose. It’s hard to take a specific database, for instance, and use it for anything else.

  • It requires storage within rigid schemas. Most structured data is stored in data warehouses, which means it’s hard to make changes to the data and scalability isn’t simple.

  • Cannot increase storage capacity. Relational databases cannot easily grow their storage capacity to store growing structured data as it hurts the query and in-turn application performance.

What is unstructured data?

Once you understand what structured data is, it’s pretty simple to grasp the concept of unstructured data—it’s basically everything else. That includes all data that doesn’t fit neatly into a row and column format, such as audio recordings, video footage, images, social media postings, email content, customer service chat transcripts, machine sensor data and much more. In fact, Gartner estimates that unstructured data makes up about 80% of all enterprise data, and some other estimates are even higher.

Benefits of unstructured data

The challenge of extracting its value is worth it. The reason Big Data analytics is such a buzzword is that the exciting possibilities posed by delving into vast stores of unstructured data. Through advanced data analytics and data mining, enterprises can process their unstructured data to identify customer purchasing behavior, for instance, according to season or time of day. Or analyze drivers’ travel patterns on highways across the city to identify where, when, and why bottlenecks are occurring. Or process social media posts to gain an understanding of the ways customers perceive a brand or how they feel about a specific product. Or perform predictive analytics on machine data, etc.

The insights from analytics have the potential to revolutionize an organization’s operations and services. With a deep level of insights into the data, enterprises can gain a seriously competitive edge, identify opportunities for new revenue streams, and boost customer service like never before, reduce maintenance costs and downtime to name a few.

Characteristics of unstructured data

As its name suggests, unstructured data has no predefined data model and traditional data tools developed for structured data can’t process or analyze it.

Instead of being stored in relational databases in data warehouses, unstructured data is often stored in its raw forms in personal thumb drives, local servers, data lakes, etc. It takes specialized, advanced tools and solutions to analyze this type of data and extract value in the form of actionable insights into every aspect of enterprises, machines, processes, etc.

Examples of unstructured data

Unstructured data can be generated by humans or machines. Human-generated information can include audio files, videos such as YouTube content & surveillance, photos, healthcare imaging and text messages. Machine-generated data can include sensor data from turbines, aircraft engines, IOT, appliances, system logs, traffic or weather, for instance, or satellite imagery, digital surveillance files, or atmospheric data.

Unstructured data is used in cases where you want to perform predictive analytics, detect anomalies in machine data or user/customer behavior, determine qualitative characteristics, such as public opinion or product effectiveness. Other use cases include:

  • Prevent, detect or recover from cyber attacks by analyzing data for anomaly behavior

  • Perform predictive analytics on machine data to reduce maintenance costs and downtime

  • Analyze audio/video customer interaction transcripts to improve support and customer satisfaction

  • Analyze application ingested data to improve performance

  • Measuring the effectiveness of a marketing campaign

  • Identifying potential buying trends by analyzing social media posts and review sites

  • Detecting employee satisfaction through text mining of chats or emails

  • Enabling chatbots through text analysis, to get customers to the right resources

  • Natural language processing to determine customer sentiments about a product or brand

Pros and cons of unstructured data

Of course, there are advantages and disadvantages to dealing with unstructured data. Besides having the potential to deliver deep, game-changing insights into processes and customer habits, the advantages of unstructured data include:

  • More flexibility in how it’s used. Unstructured data is stored in its native format and doesn’t need to be defined until it is in use. That means it can adapt to all kinds of use cases.

  • It’s easy to collect. Unstructured data doesn’t need to be predefined. It can come into the organization in its raw form quickly and simply, to be handled later.

  • Storage is massively scalable. Data lakes can scale easily as volumes of data grow.

Disadvantages include:

  • Need for specialized data science skills. Unstructured data can’t be analyzed or processed by just any employee. Its undefined nature and wide range of formats requires an understanding of the data itself as well as how it relates to other data.

  • It requires specialized tools. The analysis of unstructured data is still fairly new, so the available tools and platforms to organize, manage, and analyze that information are still being refined and perfected.

  • Not easy to scale storage. Storing and managing such unstructured data requires files and objects storage, along with a variety of business intelligence and analytics applications. Traditional storage cannot store and scale with massively growing unstructured data.


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The difference between unstructured data vs structured data

It’s obvious from the sections above that structured and unstructured data are very different from each other and require different tools to manage and process.

One simple way to understand the difference is to say that structured data is typically considered quantitative data—highly organized and formatted and easy to search in relational databases—that can help enterprises discover who, where, and when. It gives users the “30,000-foot view” of customers, for example. Unstructured data, on the other hand, is qualitative data—undefined, unformatted, and tough to search and process—that can help answer questions of how and why. It gives users much deeper insight into customer behaviors and even intentions.

Data management

Structured data is easy to organize and process. It’s typically all text-based, predefined, and fits perfectly into the rows and columns of relational databases. Unstructured data can come in a wide variety of formats, from audio to video to text to images and more, and it is harder to organize and process.

Data storage

As previously mentioned, structured data is organized into relational databases and often stored in data warehouses with exacting storage formats. Unstructured data is a jumble of many different formats and file types, and often stored in data lakes, which doesn’t require any sort of predefinition or formatting.

Data analysis

Structured data analytics is a very mature process that has been around long enough for developers to create many effective tools and platforms. Unstructured data analytics is still considered a developing industry and its tools and platforms are not as mature. It also takes specialized knowledge and skills to analyze unstructured data.

What is semi-structured data?

While structured and unstructured data are the two most common types of information, there is also a category called semi-structured data. This is basically unstructured data that comes with some metadata to categorize the information in a variety of ways. Thanks to the metadata, users can more easily categorize, search, and analyze this information, much as they would structured data.

Located between the two extremes of structured and unstructured data, semi-structured data doesn’t conform to a fixed or rigid data schema, but it still has a semblance of structure. The metadata is the key that makes semi-structured data easier to search, store, and organize than unstructured data.

Similar to unstructured data, the lack of structure makes semi-structured data difficult for machines to parse.

Examples of semi-structured data

A very common use case for semi-structured data is email content. While emails can’t be organized into a relational database, it does have inherent metadata that enables users to search for keywords without the need for more advanced tools. Other use cases focus on simplifying data transport, such as sharing sensor data, electronic data interchange (EDI), and document markup languages.

Nutanix data management solutions

With intelligent, scalable, hybrid cloud solutions designed specifically for today’s enterprises, Nutanix has a range of solutions that can simplify the storage, management, and analysis of your structured and unstructured data. These solutions include:

  • Nutanix Cloud Platform, which now delivers unstructured data tiering from on-premises to cloud, as well as a 2x storage performance increase for database workloads and 3x for big data workloads—all without requiring complex reconfiguration.

  • Nutanix Unified Storage - software-defined storage platform that consolidates seamless access and management of siloed block, file and object storage into a single platform. Leveraging the Nutanix Cloud Platform, NUS is built for scale, performance, and integrated data security requirements of modern applications deployed on core, cloud, or edge.

  • Nutanix Files - software-defined platform for files storage eliminating storage silos and simplifying management with a single-click automation helping customers to scale easily without compromising performance. With integrated cyber security and ransomware protection Nutanix Files is uniquely positioned to protect your unstructured data.

  • Nutanix Objects - a simple and scale-out S3 compatible object storage for modern cloud native and big data applications that is easy to use, high performance, secure and flexible for a multicloud deployment. Nutanix Objects is a unified, flexible platform that stores file, block, VM, and many other types of workloads.

  • Nutanix Volumes - Volumes Block Storage that bridges the physical and virtual infrastructure, combining them into one unified platform with the simplicity that enterprises have grown to rely on.

  • Nutanix Data Lens, our unstructured data governance service that can help simplify data lifecycle management as well as protect against ransomware attacks.

  • Nutanix Database Service, a database service that delivers one-click storage scaling and rich role-based access control for database management across hybrid multicloud environments for database engines like PostgreSQL®, MySQL®, Microsoft® SQL Server, and Oracle® Database.

The future of data

What does the future of data look like is structured or unstructured taking over? Will a new form be introduced? 

One thing is clear even today – unstructured data volumes are growing exponentially. Gartner and other industry analysts already estimate that unstructured data makes up anywhere from 80% to 90% of all the world’s data, and advanced technologies such as AI will only ensure that that percentage goes even higher in years to come. 

But that’s not to say that structured data will go away. It still has value and will likely hold on to that value for the foreseeable future, at least. Structured data is the realm of customer account information, names and addresses, bookkeeping information, inventory counts, network and server logs, and other essential facts and figures. It’s also the type of data that works best for machine learning. 

For some time now, there has already been a third data type that has formed a sort of bridge between structured and unstructured data. It’s called semi-structured data and it has a few advantages of each of the other types and a lot of unstructured data today is being considered semi-structured. 

However, it hasn’t overtaken either of the other types in terms of making structured or unstructured data obsolete – and no one thinks it will do so in the near future. 

So while we might not necessarily see a new type of data emerging, what IS evolving are the many data storage and management solutions. Vendors are recognizing the inherent differences between structured, unstructured, and semi-structured data and are investing heavily into creating solutions that can make it easier to store, access, analyze, manage, and share all types of data without compromise. 



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